Latest

Robotics can manipulate rigid objects, but deformed objects remain a challenge in embodied AI. Objects such as clothing, cables, ropes, bags, and soft materials change shape during interaction. Their behavior depends on gravity, contact forces, friction, and previous states, making perception and manipulation more complex. Robot foundation models require specialized

The rapid advancement of AI and robotic technologies has significantly transformed modern healthcare, particularly in surgery. AI-assisted surgical robotics systems are increasingly being integrated into clinical practice to enhance procedural accuracy, support decision-making, and improve patient outcomes. However, the effectiveness and reliability of these intelligent systems depend heavily

Warehouse premises are among the most complex environments for artificial intelligence systems, where standard computer vision algorithms trained on classic open datasets regularly fail. The main challenge here is the organization of the assortment, where thousands of visually identical boxes differing only by a serial number on a tiny barcode

Modern robotics relies on simulation environments to accelerate AI development. Rather than relying on physical robots operating in real-world environments, organizations are generating large amounts of synthetic training data in virtual worlds. Simulation platforms allow for experimentation, testing, and large-scale scenario generation. However, a challenge in robotics remains

The rapid development of robotics, AI, and machine learning technologies contributes to the creation of autonomous systems capable of effectively interacting with the environment. One of the key tasks of modern robotics is to ensure reliable capture and manipulation of objects under conditions of uncertainty. To successfully perform such operations,

Unlike simpler automated systems, a humanoid robot's perception is built on a complex fusion of dozens of disparate information streams that must be synchronized in time with millisecond precision. The process of collecting and preparing data for such machines goes far beyond the framework of classic two-dimensional

Instead of manually programming robot behavior, robots are trained using human demonstrations, teleoperation sessions, and multimodal interaction data. This approach allows robots to learn complex manipulation, navigation, and decision-making skills. At the heart of this are simulation learning and scalable annotation datasets. They help machine learning models understand which